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Data-Glove Optimization

INTRODUCTION

Background

In recent years, sensory data gloves have been extensively used along with the popularization of virtual reality (VR) applications. The data glove is a multisensory device that generates a large amount of data and is more complex than other input devices. However, most researchers still adopt the data glove because its natural interfacing characteristic with the human being is the way to improve system robotic control [1], sign language recognition systems [2] surgery training of medical applications [3] entertainment sports of VR systems [4] industrial manufacturing of CAD/CAM applications [5].

A common device for recognizing hand gestures in virtual environments is a pair of virtual reality gloves. Virtual reality gloves can be light and flexible, rendering them comfortable for extended use and are being produced for long enough to offer a stable and quite affordable solution for gesture recognition. There are so many data gloves have been invented for different purposes. Under mentioned and common data gloves are design for specific purposes that kind of gloves may be used only specific task.

Common Data Gloves

CyberGrasp data glove.

Since pinch gloves report only contact between fingers (single touch) they can be employed to recognize postures instead of gestures. To this end, the technique devised for whole hand data gloves cannot be directly applied. Instead we propose a technique similar to the one used by children when they learn to count [4], [5]. Using the index of the dominant hand, they count the fingers of the non-dominant hand (one to five), and then they use (if the number is greater than five) the index of the non-dominant hand to count (six to ten) the fingers of the dominant hand. This technique cannot be directly implemented as it has an inherent ambiguity: touch between the indexes of the two hands may denote a 2 or a 7. To overcome this limitation/problem the method proposed uses the index of the dominant hand to touch fingers denoting numbers one to five and the middle finger to denote numbers six to zero.

Sensor Technologies Used

Sensors allow detection, analysis, and recording of physical phenomenon that are difficult to otherwise measure by converting the phenomenon into a more convenient signal. Sensors convert physical measurements such as displacement, velocity, acceleration, force, pressure, chemical concentration, or flow into electrical signals.

Figure 1.1 Data glove Example

Human movement tracking systems can be classified as inside-in, inside-out and outside-in systems.

Inside-in systems are defined as those which employ sensor(s) and source(s) that are both on the body (e.g. a glove with piezo-resistive flex sensors). The sensors generally have small form-factors and are therefore especially suitable for tracking small body parts.

Inside-out systems employ sensor(s) on the body that sense artificial external source(s) (e.g. a coil moving in a externally generated electromagnetic field), or natural external source(s).There workspace and accuracy is generally limited due to use of the external source and their form factor restricts use to medium and larger sized body parts.

Outside-in systems employ an external sensor that senses artificial source(s) or marker(s) on the body, e.g. an electro-optical system that tracks reflective markers, or natural source(s) on the body (e.g. a video camera based system) .Due to the occlusion it is hard or impossible to track small body parts .The optical or image based systems require sophisticated hardware and software and are therefore expensive [7].

Accelerometer Sensors

Accelerometer sensors measure the acceleration experienced by the sensor and anything to which the sensor is directly attached. When working with accelerometers in the earth’s gravitational field, there is always the acceleration due to gravity. Thus the signal from an accelerometer sensor can be separated into two signals: the acceleration from gravity, and external acceleration. The acceleration from gravity allows measurement of the tilt of the sensor by identifying which direction is “down”. By filtering out the external acceleration, the orientation of a three-axis sensor can be calculated from the accelerations on the three accelerometer axes. Orientation sensing can be very useful in navigation [8].

Pressure measurement

A hand pressure threshold sensor generates a signal representative of the application of excessive force to a hand held instrument to which the sensor is coupled. By using the sensor, a person having impaired tactile sensibilities, such that he is unaware of the degree of force he is applying [9].

Figure 1.2 Flexi Force mounted to hand bones

Pressure measuring sensors used whenever pressure force between the human body, (e.g. hand, finger and foot) and any surface is investigated. The use of industrial tools, training machines, computer devices (PC mouse) or medical orthotics can be investigated together with EMG and other biomechanical sensors.

Muscle tension measurement

Indeterminacy is the primary obstacle for inverse kinematic modeling in musculoskeletal biomechanics. Force transducers and EMG provide clues of muscle load sharing. Magnetic Resonance Elastography (MRE) is a new technique for quantifying tissue stiffness in vivo. Muscle stiffness has been shown to change with state of contraction.MRE applies shear waves to muscle and images the wave propagation through the tissue. Imaged wavelength changes with muscle stiffness and is therefore directly related to muscle tension. MRE can directly measure isometric muscle tension [10].

Blood pressure measurement

The hydrostatic pressure changes at the sensor correlating the change of pulse wave velocity to the hydrostatic pressure sensor. This will measure the blood pressure [11].

Figure 1.3Wearable blood pressure monitor

Identification of Hand Parameters

The human hand is an essential part of human form, function, and communication, capable of complex, expressive articulation. Its complicated neuron-physiology makes it a formidable challenge for animators to emulate. Most computer graphics research on hand motion has focused on grasping and gestures with application to areas of robot planning, prosthetics, human computer interaction and sign language

Studies on the limitations and constraints of the various joints of the hand are well understood. The interdependency of the various joints, however, is largely based on empirical observation. It is that interdependency that we strive to capture in the hand model. The currently accepted theory for the cause of sympathetic movement in the hand is due to a combination of biomechanical and neurological constraints. Biomechanical restrictions are partially due to the muscle and tendon configuration. Muscles, such as the Extensor Digitorum Communis in the forearm have insertions in multiple joints. The activation of such muscles thus results in the excursion of multiple tendons. The tendons also restrict each other’s motion due to their configuration and close proximity. Neurological constraints are also believed to contribute to the sympathetic motion. Understanding the neuron-physiology of the human hand is still an area of active research and not yet matures enough to construct an anatomically complete model for sympathetic hand motion [12].

Figure 1.4 Human hand cloning from surface anatomy [13]

The human hand has 27 degrees of freedom: 4 in each finger, 3 for extension and flexion and one for abduction and adduction; the thumb is more complicated and has 5 DOF,leaving 6 DOF for the rotation and translation of the wrist 1.To accurately model the hand, a complete model of the

Muscles, tendons, bones and a neurological control structure are necessary. The dynamics of such a complex model are still poorly understood, forcing the use of simple models.

Current models are too simple for our purpose so we turn to recent work from the medical community to motivate assumptions used in a new model that we propose here.

So far paper described on current data gloves and their applications as well as sensors which commonly used on data gloves. Our interest is to implement customizable data glove for different kind of applications such as virtual reality, robot arms as discussed as earlier. In current situation there are no customizable data gloves to use different usages. So we interested on human hand and identified their behavior and its realistic model to create high sensitivity low cost data glove.

Problems Under Exploration

Literature Review

Data glove is a new dimension in the field of virtual reality environments, initially designed to satisfy the stringent requirements of modern motion capture and animation professionals. At present, the data glove has been increasingly employed in the areas of teleoperations and robotic control [1], sign language recognition systems [2], surgery training of medical applications [3], entertainment sports of virtual reality systems [4], industrial manufacturing of CAD/CAM applications [5], and so on.

David J Sterman and David Zeltzer [14] describe in roughly chronological order the more significant Hand-tracking gloves that appear in the literature or market place such as, Sayre Glove, MIT LED Glove, Digital Data-Entry Glove, Data-Glove,Dexterous HandMaster, Power Glove, CyberGlove, VPL Glove, and Space Glove. Yunli Lee1, Seungki Min1, HwangKyu Yang2, and Keechul Jung [15] introduce a motion sensitive glove-based Korean Finger spelling Tutor using a designed data glove of two tilt sensors, five flex sensors and three pressure sensors.

These witness that several kinds of sensing technology have been realized and applied to the development of a data glove. Most of them are subject to provide high accuracy, high reliability, and high capability in measuring the degree of freedom (DOF) of human hands due to the application. There are a number of different technologies being investigated for a wide variety of purposes. Axcel Mulder [7] discuss of currently available sensing technologies for tracking human movement and of their general issues. The use of three-dimensional accelerometer sensor for measuring involuntary human hand motion [8], measuring forces over the entire hand using thin-film sensors and associated electronics [9], hand pressure threshold sensor that generates a signal representative of the application of excessive force [10] are some of the sensor technologies related with data gloves.

Majority of the data gloves are built with flex sensors attached on the finger joint positions of the hand. When the fingers are bent, the sensors are also flexed and the generated outputs are measured. Then, the bending angles of the fingers are calculated. When users wear the data gloves, the stretching and bending of the finger joints very often occur. It leads to a reduction in the lifetime of the sensors and the accuracy of measurement. Chin-Shyurng Fahn and Herman Sun [16] address this problem by the development of a dataglove system using magnetic induction coils as finger movement sensors. These sensors are installed on the finger phalange positions; there is no contact point between the sensors and the finger joints. Hence, the shape of the sensors does not change as the fingers are bending, and the quality of measurement and the lifetime of the sensors will not decrease with time.

Human hands and fingers are sensor rich, which allow us to pick up, stably grasp and manipulate objects and tools. Despite many successful high-level skeletal control techniques, animating realistic hand motion remains tedious and challenging.G ElKoura [12]presents research motivated by the complex finger positioning required to play musical instruments, such as the guitar and provides the general architecture for the skeletal control of interdependent articulations performing multiple concurrent reaching tasks.T Rhee[13]present a method for constructing a person-specific model from a single canonically posed palm image of the hand without human guidance and a 3D model of an individual’s hand, with similar joint locations, contours, and skin texture. In fact, most of the work on hand-shape recognition is done for communicative gestures, such as pointing motions, symbols, or sign languages, and cannot be applied directly to manipulative gestures or grasps. However grasping is addressed somewhat in [7] for developing robot hand grasping algorithms by the analysis of human hand grasping operation based on human knowledge and experience.

An important aspect of human hand motion comes from how it combines passive and active control. So with proper identification of parameters we can create motion similar to motion capture data, and demonstrate plausible compliance when acquiring the grasp in the suggesting virtual hand model. Our object will be on designing a customizable data glove which produces hand motion with the richness of motion capture data with the proper identification of parameters for the maximum input of data.